Techniques of deep learning and image processing in plant leaf disease detection: a review

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 13, No. 3, June 2023, pp. 3029~3040
ISSN: 2088-8708, DOI: 10.11591/ijece.v13i3.pp3029-3040  3029
Journal homepage: http://ijece.iaescore.com
Techniques of deep learning and image processing in plant leaf
disease detection: a review
Anita S. Kini1
, Prema K. V. Reddy2
, Smitha N. Pai3
1
Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education,
Manipal, India
2
Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education,
Bangalore, India
3
Department of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education,
Manipal, India
Article Info ABSTRACT
Article history:
Received Feb 19, 2022
Revised Jun 17, 2022
Accepted Jun 21, 2022
Computer vision techniques are an emerging trend today. Digital image
processing is gaining popularity because of the significant upsurge in the
usage of digital images over the internet. Digital image processing is a practice
that can help in designing sophisticated high-end machines, which can hold
the ophthalmic functionality of the human eye. In agriculture, leaf examination
is important for disease identification and fair warning for any deficiency
within the plant. Many prominent plant species are facing extinction because
of a lack of knowledge. A proper realization of computer vision techniques
aid in extracting a significant amount of information from leaf image. This
necessitates the requirement of an automatic leaf disease detection method to
diagnose disease occurrences and severity, for timely crop management, by
spraying pesticides. This study focuses on techniques of digital image
processing and machine learning rendered in plant leaf disease detection,
which has great potential in precision agriculture. To support this study,
techniques exercised by various researchers in recent years are tabulated.
Keywords:
Convolutional network
Deep learning
Digital image processing
Leaf diseases
Segmentation methods
This is an open access article under the CC BY-SA license.
Corresponding Author:
Prema K. V. Reddy
Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of
Higher Education
Bangalore, India
Email: prema.kv@manipal.edu
1. INTRODUCTION
Farmers’ revenue is an essential and important trait for any country. All the resources directly or
indirectly are associated with agriculture and its product. Thus, improvement in agri-farm products becomes
significant for the overall development of the country [1]. Plants are important food resources; they are also an
important part of the food chain. Some medicinal plants are rare and extinct, which need to be preserved. Plant
disease detection is an essential chore to maximize the farm productivity and survival of rare species. It is the
basic imperative task, as the crop yield gets affected due to various plant diseases. Plant diseases are categorized
as biotic and abiotic. Abiotic diseases are caused due to non-living things [2], [3]. Such as ecological
circumstances, weather conditions, and spring frosts. Most abiotic diseases are preventable, least transmissible,
not communicable, and harmless. Biotic diseases are caused due to living organisms such as bacteria, fungi,
and viruses. Various forms of bacterial, virus, and fungi related plant diseases are shown in Figure 1. The most
prominent diseases found in leaves are anthracnose, leaf spot, slow wilt, and quick wilt. Accurate disease
identification is essential to assist in appropriate fertilizers usage [4]. Most of the research work done in the
identification and classification of plants is based on their physical appearance. The traditional approach to

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diagnosing disease is based on the ideal laboratory conditions with limited resources [5]. For this, many factors
are taken into account varying from plant length, appearance, leaf color, petals, stem, and root. It is realized in
the given study that the plant's leaf is self-sufficient to provide reasonable details of the plants' complete health
[6], [7]. Plant leaves are available throughout the year, are easier to be photographed digitally, and simpler to
enumerate in two dimensions.
Figure 1. Digital image processing
2. METHOD
The main motivation for this research work is to provide a clear and precise understanding of the most
prominent deep-learning methodology used in recent years in leaf disease detection. This study will help
researchers to acknowledge various sources of data, devices used for gathering data, preprocessing, post-
processing, and segmentation methods used in recent years. The comprehension is provided in the form of
tables. The researchers should know the most successful methods used and how the said method performs.
To have a better understanding segmentation technique that provides high output are discussed with
their advantages and limitations. Machine-learning techniques are classified highlighting outcome-based
review, over the recent practice. Through this paper, readers would motivate to implement high-end techniques
like deep learning for other plant diseases present in fauna flora not explored so far.
3. DIGITAL IMAGE PROCESSING
Digital image processing is a division of computer vision systems that helps in designing sophisticated
high-end machines that can hold the ophthalmic functionality of human beings. It is a prominent field in
agriculture for plant species detection, disease detection, biological processing, and many other scientific and
engineering fields. Digital image processing is a systematic approach to digitizing the pictorial representation
in the form of elements known as pixels. This includes enhancement, filtering, and segmentation [8]–[10]. The
main aim of digital image processing is to enhance important parameters and compress irrelevant information
about the object of interest. Classification of plants is required to segregate useful and not-so-useful plants.
Plants classification done manually is laborious, time-consuming, and in the absence of an expert incorrect.
This has influenced many researchers to conduct experiments to automate the process in the agriculture unit in
its entirety. Agricultural sustainability in terms of productivity can be retained with steadfast precise techniques
[11], [12]. There are wide techniques available in computer vision to provide better accuracy, precision, and
recall faster than the traditional approach, which is the motivation to come up with this study. An effort is made
to represent the review in a significant way. This paper is organized into sections that discuss the digital image
process, machine learning techniques, and convolutional neural networks (CNNs) by segregating the relevant
information in tabular form for clear vision.
Images are the pictorial representation, which we humans can view through the naked eye.
Fundamentally, digital images can be represented in three basic type’s binary, grey, and red-green-blue (RGB)
images. The secondary colors such as hue-saturation-value (HSV), YCbCr, L*a*b*, hue-saturation-intensity
(HIS), cyan-magenta-yellow (CMY), greyscale, and CIE [13], [14] are color transformed as per the need of the
experiment. The digital image represents many facets, extending from image acquisition, digitization,
enhancement, or subsequent processing by exhibiting mathematical characterization for image display.
Figure 1 shows the basic model of digital image processing with sample examples in each of the phases.

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Techniques of deep learning and image processing in plant leaf disease detection: a review (Anita S. Kini)
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3.1. Image acquisition
Image acquisition is a process of data collection. Data acquisition is done using an online image
repository [15], [16] or laboratory assessed. The most trending technique nowadays is live image capturing.
This trend is emerging in agriculture, as there is a lack of datasets relating to the study of research interest [17].
Table 1 shows the image acquisition carried out by recent researchers systematically.
Table 1. Data sources and pre-processing techniques
Data set/source No of
images
Device Back-
ground
Pre-
processing
Post-processing Thresholding
method
Reference
Real captured
image
900 Digital Camera Natural/Co
mplex
Binary
conversion
Thresholding Histogram
Based
[5]
Captured 560 Nikon Coolpix Complex HSV Three different
color space
Gaussian [7]
Field captured
image
720 Samsung White
paper
Automatic Radial Map Otsu [8]
UoM 1320 Camera White Average F
measures
MLBP Otsu [10]
Flavia, Foliage,
Swedish
1320 Online White LBP MLBP Average
F-measures
[10]
Captured 227 Camera Plain White Clustering Binarization Otsu [14]
Dummy data 82 Created NA 2-D Spatial Erosion
technique
Thresholding [18]
In field 150 Digital camera and
smart telephone
Gray Denoising SR based
classification
PHOG [19]
Online 75 Public repository Plain Denoising Cluster Otsu [20]
Captured 4923 USB Camera Black/Whit
e
Image
Enhanceme
nt
Classifiers NN [21]
Plant village ~4309 Plant Village Gray Deep
learning
NN Classifiers Alex Net/
Squeeze Net
[22]
Satellite images 5932 Nikon DSLR-D5600 Gray CNN CNN Neural
networks
[23]
Camera
captured
1443 Smart
Phone/Camera
Complex CNN CNN Neural
networks
[24]
Plant village 1000 Digital camera White Gray level
matrix
ReLU Cost Matrix [25]
Captured 5000 Camera, mobile
device
hard
negative
Image
Enhanceme
nt
Deep
learning/CNN
Meta
Architecture
of CNN
[26]
Plant village 54306 Online Black -N -N Grey Level [27]
Plantvillage.org/ 14,208 (Nikon Coolpix S3
100)
Complex DCNN DCNN Deep neural
networks
[28]
PlantClef2015 272892 Repository Plain CNN CNN GPU [29]
Plant village 54305 Plant Village Gray K-NN CNN Deep
learning
[30]
Captured 15620 Canon EOS550 D White CNN C NN ResNet [31]
In field 150 Canon A640 digital
camera
Complex k-Means PNN PCA+ Neural
networks
[31]
PPBC 1000 Repository Complex RPN CNN CV algorithm [32]
3.2. Preprocessing
The purpose of image processing is to split an image into significant, non-overlapping regions. The
major step in image analysis is segmentation. Before segmentation, images are pre-processed to remove noise
i.e., unwanted disturbances. Preprocessing is the basis and primary procedure for training data individually for
image processing. It is done by processes called smoothing or enhancement, color space conversion, and
filtering [12]. It is the main step to enhance and improve the image vision. With high-end computing techniques
plant, (crop) disease identification and detection could be realized indirectly [33]. The direct implementation
method includes serological and molecular techniques. Which is done by a pathologist in the laboratory and
by an agriculture-based researcher. Indirect disease detection diagnosis such as computer vision techniques is
used to gain high throughput for large datasets [34]. Figure 2 depicts the process of disease detection in rice
plants through digital image processing and testing. The quality of feature extraction and the result of image
analysis using the image preprocessing method have a very intense positive effect [35]. It is corresponding to
the mathematical normalization of the data set. Pre-processing is done in two steps, first to preserve pixels as
it is, and second use filtering [36]–[38]. Preserving images, as it is results in a tedious analysis as all that images
may be of varying size and parameters [18], [39]. Hence, a proper filtering technique is used to extract desired
features. The general technique involves the conversion of one input feature to other output features [40], [41].

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Figure 2. Phase of images testing of Rice plant leaf [3]
Other prevalent primary processing techniques involve translation in color expanses, procurements,
leveling for filtering, and enrichment of specific features in the crop. Image conversion facilitates the removal
of the shadow effect in the images taken under natural environment conditions [19], [42]. As per the review,
the color image has a comparatively better image output than the grayscale. However, for diagnosis of leaf
disease, RGB gives clearer and noise-free output compared to grayscale images [43], [44]. The dull or blur
image needs to be brightened. The images are manipulated so that they are more suitable for the specific
application [45]. Discrete cosine transform is a technique applied for leaf image enhancement. Enhancement
is the process to brighten the stultified part of the image and give it a lively, clearer appearance like the original.
Histogram equalizers are applied in plant leaf disease diagnosis as an image enhancer. The background removal
process is simplified after an image is enhanced. Cropping is the basic practice to adjust images as necessary.
Generally, the acquired image is either downloaded or taken in the real environment with the help of a camera.
These images are not of the same size and resolution. It is required to change the aspect ratio of images to
collectively form a dataset [46]. Cropping is a technique mostly used to eliminate peripheral unwanted noise or
unwanted parts from the image. The concept called auto cropping can be combined with any other technique such
as fuzzy clustering to form sharper classifications. Tools are available to crop images in rectangles, squares,
polygons, and other forms.
3.3. Image segmentation
Pre-processed data are segmented through different techniques such as edge-based, region-based,
threshold-based, and clustering-based. The focus here is to extract informative data from the pictorial
representation [20], [47]. The images can be brightened, blurred, enlightened, changed contrasts or changed to
another color format [21], [48]. During the segmentation procedure, the background of the image is crucial.
Under a natural backdrop, the image is supposed to be complex. The distinction between the required and
unwanted parts of the image becomes difficult. There are optimal chances that there will be hard negative
features, which are not so-useful entities. So as good practitioners, researchers remove the background or
convert the background image to white or black. Some papers, also suggest grey background to suit the choice
of image color conversion done [49], [50].
3.3.1. Low level segmentation
Low-level segmentation is the segmentation done directly at pixel levels. The techniques used are
thresholding, edge based, and region based.
a. Thresholding
Thresholding is a technique where an image is converted from one form to another form of color. That
is from color to grey, RGB to HSV, RGB to YCbCr, RGB to CMY, grey to binary, and so on. This is done to
reduce the noise as well as the shadow effect in the image. Otsu thresholding [22], [51] algorithm is one such
age-old technique. The algorithm is very robust, and strong, and provides accurate results. Due to its high
accuracy and clear differences, this is popular until today and is the primary choice among scholars.
b. Edge-based
To correct the periphery of the image contours, edge detection is done. In this method, the corners of
the images are checked for any discontinuity or breakage [52]. This is the means of finding any default related
to the image edge to differentiate one object from the other in the image. This method gives the boundary-
related details of pixel intensities in the image. Lesion segmentation gives a significant result in cucumber leaf
image diagnosis.
c. Region-based
The spot detection practice in unhealthy plant leaf diagnosis elevates the chances of rectifying and
verifying the disease symptoms appropriately. The spot uncovering procedure can be based on regions
separated as normal regions, spot regions, background regions, or growing regions. These techniques are
crucial in segregating the disease from the core region. The region is narrowed down as a region of interest

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[52]. The functionality of region-based segmentation is similar to edge-based segmentation. Here the
neighboring pixels are also considered to form a group of pixels near to each other so that it stands out as a
sub-region i.e. region within the image [53]. This region may further grow based on predefined criteria. Region-
based color differentiation can be highlighted as that of undesired areas. Consider image f segmented into more
homogenous regions 𝑅𝑖, 𝑖 = 1 … 𝑛. An f image can be segmented into regions Ri, such that:
𝑓 = 𝑈𝑖
𝑛
𝑅𝑖
𝑅𝑖Ո𝑅𝑗 = ɸ, 1 ≤ 𝑖, 𝑗 ≤ 𝑛, 𝑎𝑛𝑑 𝑖 ≠ 𝑗
𝑃(𝑅) = 𝑇𝑟𝑢𝑒 𝑓𝑜𝑟 𝑎𝑙𝑙 𝑖
𝑃(𝑅𝑖 ∪ 𝑅𝑗) = 𝐹𝑎𝑙𝑠𝑒 ,1 ≤ 𝑖, 𝑗 ≤ 𝑛, 𝑎𝑛𝑑 𝑖 ≠ 𝑗, 𝑎𝑛𝑑 𝑅𝑖 , 𝑅𝑗 𝑎𝑟𝑒 𝑎𝑑𝑗𝑎𝑐𝑒𝑛𝑡
where P(R) is considered to the logical predicate well-defined over-all points in R space. This is true for the
pixels inside the region and false outside the region.
3.3.2. High level segmentation
The segmentation process is ruminated at a high level when instead of pixels, direct objects of the
images are considered. Such as clustering-based segmentation. These channels to the usage of the data at the
larger level where instead of the object-pixel elements alone the neighboring pixel are also considered. Such
that they form similar group elements and are formed as clusters [23], [54]. Clustering-based segmentation is
like detecting similarities in the regions of the given image or image under test. Most authors have
recommended k-means clustering for the segmentation of images. Some segmentation is done separately by
means of mathematical Euclidean measurement of space difference. Zhang et al. [19] used orthogonal locally
discriminant projection (OLDP) an orthogonal nonlinear dimensionality reduction algorithm for validating
symptoms of plant disease. The clustered out-turn is fed to neural network classifiers. The segmentation
techniques are cited in Table 2, which is reviewed through the reference papers.
Table 2. Segmentation methods reviewed in papers with advantage, limitation, and critical comments
Segmentation
method
Segmentation
level
Advantage Limitation Critical comment Ref.
Otsu Low Clear thresholding Wrong selection may result
improper segmentation
Effective erosion operation [8]
MLBP High High accuracy, nearest
possible pixel difference
May sometime give nearest
neighbor error
Effective for clustering
techniques
[10]
Enhanced
snowmelt runoff
model (SRM)
Low Better clarity and noise
free
All colors need to be
considered
RGB scale gives better clarity
than gray scale
[12]
K-means High/Complex Better classification Can only be applied for two
or more classes
Gives clear generalization [14]
Enhanced SRM High Better suited to human/
machine Interpretation
Works well with RGB image
and not with grey scale
Suited for machine learning [15]
K-means
clustering
High Easy to analyze More number of clusters
does not give accurate result
Maximum 3 cluster will give
effective result
[33]
Binarization Low Extracts relevant
information
Restricted application Small imperfection details may
not be visible
[18]
Ada-Boost High Accuracy similar to
human vision
Differentiating background
similar to disease symptoms
difficult
Technique can be matched to
human expert in real time
[42]
K-means, user
pixel
Medium/fairly
Simple
Enhances smoothening/
denoising
Sensitive to the initial
centroids and the number of
clusters
Only generates enhanced
results when the initial
centroids are close to the
desired solution
[42]
K-mean
clustering
High Images clustered into
different sectors
Not suitable for binary Classifier model advent to
cluster the images
[20]
HOG High Gives high accuracy High computational power High computation but with
high accuracy
[55]
Deep networks High Faster processing Pure background/Controlled
environment
Laborious task, consume more
time/may not be efficient
[56]
Region Growing Low Partition an image into
regions
Seed regions are marked.
Non-seed regions considered
after repeated analysis.
Simple approach, determines
whether pixel need to be added
examining neighbor pixels
[57]
3.4. Feature extraction
The output from the segmentation stage is followed by the feature extraction phase. Feature extraction
relates to either feature finding or feature description. This phase is useful to reduce the data size without losing

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its property. Feature finding has three major components as color, texture, and shape. Imaging techniques are
brought into use to do online rectification. These processes use hyperspectral imaging [25], [58]. It is a non-
destructive means to check medicinal plants in industries. Feature description gives more detail about the
image, relates to the quantitative image attributes, and is very useful in detecting moving pictures its relativity
is with the region or boundary of the image. This stage is important from a dimensionality reduction point of
view, where the raw data is converted to manageable groups or chunks for further processing. This speeds up
the processing methodology [26]. A fully connected layer in a neural network automates the process by acquiring
large features from the training set without accounting for spatial features in the image in deep learning practices.
3.5. Classification
Classification in plant disease detection refers to symptoms and their pattern. Each disease in a plant
has its specific pattern. Segmentation is considered to be the most difficult part of image processing as input to
computer vision depends solely on the quality of the segmented output. For a few cases, partial classification is
desired. Researchers have used color segmentation and used a self-organizing map for further classification,
such as that for cucumber leaf disease detection and tomato leaves [52]. Visual analysis is simple and the least
expensive. However, is not an efficient method. There are some areas where expertise is always advisable. Like
in agriculture for plant disease detection. Where manual analysis may result in the wrong treatment of crops and
the wrong usage of chemicals if not done by an experienced and expert person. Classification is finally the
predicament where we start labeling or classifying images based on the basic extracted features [59], [60]. The
use of deep learning with image processing has come out as a splendid combination to apply in such relative
fields. Prominent machine learning algorithm implemented as classifiers are discussed in the next section.
4. MACHINE LEARNING TECHNIQUE
The ultimate aim of machine learning is to imitate humans. Machine learning is the science behind
artificial intelligence where the machine learns from the available data, pattern, or pictorial representation just
like a human does. Subsequently, the learning is implemented without human intervention [61]. When trained
the machine learns on its own. Such learning helps in customizing a large number of data from the commercial
database and reliably discovering knowledge from them. It is the fastest-growing field in computer science and
has fast-reaching applications. There has been wide use of image processing and machine learning in
monitoring leaves, harvesting, and some other phases of plants. The other prominent area in agriculture where
machine learning is implemented is monitoring harvests. Automation in field-based disease diagnosis ensures
benefits to plant breeders and growers [55], [62], [63]. This is done for disease detection, checking the invasion
of insects, or for simply monitoring production. For the large site, unmanned aerial vehicles are employed [27],
[56]. It is advisable to have early disease diagnosis in plants to reduce the total cost of expenditure in agriculture
and overcome treatment associated with environmental influence. The benefit of using advanced techniques
like machine learning with image vision is to have automated remote observing for precision agriculture where
land coverage is huge.
Machine learning techniques are divided into two major paradigms, supervised learning, and
unsupervised learning. In a supervised way, the machine is trained with labeled data and in an unsupervised
approach, the data to be learned is not labeled. These two criteria are to confer programs with proficiency to
learn and adapt to the given model. These two variants are discussed in the succeeding section. Based on the
techniques of learning adaptability, machine learning is categorized as deep learning and shallow learning [64],
[65]. Figure 3 shows the basic hierarchy.
Figure 3. Machine learning hierarchical model

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4.1. Deep learning
Deep learning is a prominent field. It is a model that can provide accurate, quick, and automatic
classification. This is the reason deep learning has gained exponential popularity [28], [66], [67]. It has become
an oppressive research topic among researchers. The deep learning model comprises input layers with a set of
neurons, an output layer with a set of output neurons, and intermediary neuron layers. In deep learning
architecture with input and output layers, there are various classifying/hidden layers that act as classifiers. The
data under study for such a model is distributed as training and testing sets [29], [68]. Three major paradigms
include the generative, discriminative, and hybrid models. The generative model, also termed parametric
density estimation, is assumed generative because the data is considered to be of the parametric type whose
value needs to be considered or understood. A generative model can be estimated based on maximum
likelihood. This is a form of probabilistic estimation. For high-end usage, the probabilistic neural network is
realized [29], [68], [69]. The equation for continuous random variable estimation considering the likelihood as
log density (with the probability of X on x, where x is a random variable) is given
𝑙(𝑆; Ɵ) = log(∏ 𝑃Ɵ
𝑚
𝑖=1 (𝑋𝑖)) = ∑ 𝐿𝑜𝑔
𝑚
𝑖=1 𝑃Ɵ(𝑋𝑖)
where S stands for training set, 𝑆 = (𝑋1 … 𝑋𝑚), PƟ = represent density distribution.
The discriminative model belongs to the conditional category i.e., logistical class model. Logistic
regression, decision tree, naïve Bayes, and Gaussian mixture model are examples of such models. Here the
classification is based on clear boundaries. The direct map is formed for unobserved variables or the target
under study. Multiple instance techniques are used in an unsupervised learning environment. A complete batch
of data is considered instead of the instances [69], [70]. Under supervised learning, there is a way to predict
(input)q on future (output)p. If pi is considered as given features of the data points and let qi be the
corresponding labels. A function f is set out with relevance to (𝑝𝑖, 𝑞𝑖) that considers p as input and results in q
as output. Such a model can be considered discriminative. Support vector machine (SVM), k-means clustering,
and maximum of traditional neural networks are discriminative models, like multi-layer perceptron,
convolution neural network, and probabilistic neural network explored in most papers. Hybrid deep-learning
models are formed when machine-learning models are integrated with other models to enhance output or to
improve expected output. The resultant model is a hybrid model. The model combination can be soft computing
based or optimized [30], [71]. The hybrid model relates to advanced machine learning techniques. Bagging
and boosting techniques are grouped to form the ensemble method, which is a hybrid model.
4.2. Shallow learning
The deep learning model is a machine learning technique that refers to unstructured data and is based
on self-learning, whereas shallow learning is a technique based on structure or labeled data. Shallow learning
includes supervised learning and unsupervised learning technique. This technique is similar to a neural network,
but the working layers are just one or two layers [31]. Low-dimensional features are extracted, and the
complexity is less as the data size is moderately small. Supervised learning is the most common and widely used
environment in machine learning methodology. The algorithm is implemented in a model where input and
output are predefined [72]. Supervised learning can be further classified as a classification model and regression
model. The supervised classification model consists of methods like SVM and random model. Unsupervised
learning is quite opposite of supervised learning. Unsupervised learning is a procedure where machines are
made to learn by themselves. It is an algorithm where machines are made to understand the pattern and interpret
the desired output by determining and providing correct classification. To be used for prediction purposes later
[73]. The main type of unsupervised-based learning algorithms is principal component analysis and clustering.
Clustering can be soft computing-based, hierarchical-based, graph theoretic-based, and partitioning based.
Hierarchical clustering can be based on a divisive and agglomerative approach.
4.3. Convolutional neural network
A convolutional neural network (CNN) is an advanced algorithm in deep learning in the amalgamation
of computer vision. It is a multilayer paradigm where each layer consists of a network of neurons. Each neuron
represents the weight and biases associated with the object under observation. It is the most efficient practice
for a huge data set. CNN is a robust technique to extract the important feature when the data size is humongous.
It reduces the parametric count of the populated variable. Because of this reason, CNN is the popular neural
network. CNN is well suited for image processing the reason being is, it provides better fitting with condensed
parameters. CNN model is applied in classing disease in maize plants and in histogram techniques to
demonstrate its importance. For tomato leaf disease identification primary CNN models such as Alex Net,
Google net, and ResNet are implemented [28], [55]. This network can be trained very well to fit in sophisticated
image analysis [32]. Table 3 summarizes prominent machine learning techniques implemented over recent

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years as per the reference papers. The main purpose of the work is to tabulate the data for better visibility of
the work done in recent years. So that all the relevant data associated with techniques in plant disease detection
is highly available.
Table 3. Summary of machine learning classifiers implemented in the study carried out in this paper
Machine
learning
classifiers
Accuracy
in %
Features Device/data source
Training
data
Testing
data
Validation
data
Reference
SVM, Neural
Network
80 Edge and color Lumia 500/Sony Xperia
XA1/Field
22 56 6-8 [4]
SVM/RBEN 90 Edge, color, texture Digital Camera/Field 900 300 100 [5]
MLP/SVM 99/98.8 Morphological, Texture Plant Repository 1200 800 400 [6]
CCF 97.29 Comprehensive color
features
Nikon Coolpix/Field 93 93 93 [7]
Random
Forest
90.1 Length, width,
perimeter, area, no of
vertices, color,
perimeters, area of hull
Camera/Real field Images 510 129 50 [8]
BPNN 98.2 ROI Digital camera/Field -N -N -N [9]
SVM 96.6 Geometric Features,
color, texture
Created 64 64 64 [18]
context aware
SVM
92 Color Digital camera/Field 150 50 50 [19]
K-mean
clustering
75 Color and texture Public repository 75 25 25 [20]
Deep Learning 95.65 Auto Identified Plant Village 54309 80% 20% [22]
DECIML 65 Edge/Angle of token Plant Village 45 20 20% [53]
Supervised
Kohonen
Networks
81.65 Map Models Satellite Data 7798 1000 1000 [54]
CNN 78 to 79 Auto generated bcch.ahnw.gov.cn 5932 200 800 [23]
9 CNN
classifiers
94.56 Color, shape, texture Smartphone/Camera/Fiel
d
1433 1012 433 [24]
CNN 95 Shape and Texture Plant Village 4000 2000 1000 [25]
CNN 86.2 Special Features BBCH 12-16 10413 10413 -N [62]
CNN 98 Auto generated Nikon D5300/Field 4742 250 180 [63]
ANN/CNN 95.5 Histogram of gradients Flavia Data Set 630 135 135 [55]
VGG-VD16 97.95 Local Features WDD2017 9230 7384 1846 [55]
Dense Net 99.75 Auto generated Plant Village 34727 10876 8702 [27]
VGG –S 95.12 Local Features WDD2017 9230 7384 1846 [56]
CNN 97.14 Colors & textures ImageNet, Plant Village 54306 60% 40% [65]
CNN 81 Disease Symptoms https://www.digipathosre
p.cnptia.embrapa.br/
50000 40000 10441 [66]
DCNN 93.4 RGB, HSV, L*a*b* Plantvillage.org/forestry
images.
8628 2292 3288 [28]
CNN 68.9 Auto selected ImageNet 50000 272892 66711 [29]
Caffe 95 Auto selected PlantClef2015 50000 272892 66711 [29]
K mean
Cluster
89 RGB, HSV In-field image 38 39 29 [57]
PNN 91.08 Statistical,
Meteorological
In-field image 100 50 50 [57]
VGGNet-16 98.44 Colors and texture In -field image 15670 11533 4137 [30]
Deep Learning 96.46 Texture, color, shape Plant Village 55,636 1950 3900 [30]
CNN 88.7 Edge and curves Camera/Plant Village 45000 3663 -N [72]
PD2
SE-Net 98 Auto generated www.challenger.ai 23381 23381 3422 [73]
RPN 83.57 Texture, shape, color,
motion-related
attributes
Repository 4714 1000 -N [32]

3037
5. CONCLUSION AND FUTURE WORK
The work displays segmentation, machine learning classifiers, and deep neural networks implemented
in plant disease detection in tabular columns. It is observed that the traditional approach of managing the crop
field is similar for most of the cases. This leads to uneven distribution of pesticides in the fields resulting in
disease mishandling. Though diseases can be predicted based on climate conditions still their symptoms cannot
be judged. According to this review, the most prominent segmenting techniques used are k-means, Gabor filter,
and enhanced snowmelt runoff model (SRM). Conventional edge detection techniques include canny edge
detection, Sobel edge detection, and Otsu method. Otsu is an age-old technique still staunch.
Real field/camera captured, online resources, and dummy data are the three major plant image data
resources. The major drawback in image analysis is the complex/busy background when images are taken
under real natural conditions. Plant leaves are self-sufficient to provide vital information, such as disease
incidence, prevalence, and severity. Interactive region growing segmentation techniques can be employed for
spot detection in plant disease. In the future, relevant techniques can be proposed to get an extensible set of
disease extraction such as the number and area of disease spots. Global features or zone-based features can be
explored. A collection of multi-angle images can be ruminated to overcome data inadequacy.
There is great real-world worth of machine learning algorithms and image processing techniques.
According to the work it is observed that the deep learning-based implementation is less investigated in depth.
This area could be further explored. The deep learning-based methodology could be used to speed up the
process when huge data are involved. The accuracy acquired using the CNN model is 94 to 98%. Deep learning-
based CNN models like VGG-16 and VGG-S can be implemented to extract higher significant accuracy to
provide sharp, quick, and automatic classification. Fuzzy clustering techniques can also be comprehended for
disease detection in plants.
ACKNOWLEDGEMENT
Authors are thankful to the Department of Computer Science and Engineering, Manipal Institute of
Technology, Manipal Academy of Higher Education, Manipal for supporting with expertise that has supported
this work.
REFERENCES
[1] H. Ye, C. Liu, and P. Niu, “Cucumber appearance quality detection under complex background based on image processing,”
International Journal of Agricultural and Biological Engineering, vol. 11, no. 4, pp. 171–177, 2018, doi:
10.25165/j.ijabe.20181104.3090.
[2] A. Singh et al., “Challenges and opportunities in machine-augmented plant stress phenotyping,” Trends in Plant Science, vol. 26,
no. 1, pp. 53–69, Jan. 2021, doi: 10.1016/j.tplants.2020.07.010.
[3] S. Phadikar, J. Sil, and A. K. Das, “Classification of rice leaf diseases based on morphological changes,” International Journal of
Information and Electronics Engineering, vol. 2, no. 3, pp. 460–463, 2012, doi: 10.7763/IJIEE.2012.V2.137.
[4] N. Petrellis, “Plant disease diagnosis for smart phone applications with extensible set of diseases,” Applied Sciences, vol. 9, no. 9,
May 2019, doi: 10.3390/app9091952.
[5] A.-K. Mahlein, “Plant disease detection by imaging sensors – parallels and specific demands for precision agriculture and plant
phenotyping,” Plant Disease, vol. 100, no. 2, pp. 241–251, Feb. 2016, doi: 10.1094/PDIS-03-15-0340-FE.
[6] P. M. Kumar, C. M. Surya, and V. P. Gopi, “Identification of ayurvedic medicinal plants by image processing of leaf samples,” in
2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN), Nov.
2017, pp. 231–238. doi: 10.1109/ICRCICN.2017.8234512.
[7] J. Ma, K. Du, L. Zhang, F. Zheng, J. Chu, and Z. Sun, “A segmentation method for greenhouse vegetable foliar disease spots images
using color information and region growing,” Computers and Electronics in Agriculture, vol. 142, pp. 110–117, Nov. 2017, doi:
10.1016/j.compag.2017.08.023.
[8] A. Begue, V. Kowlessur, U. Singh, F. Mahomoodally, and S. Pudaruth, “Automatic recognition of medicinal plants using machine
learning techniques,” International Journal of Advanced Computer Science and Applications, vol. 8, no. 4, pp. 166–175, 2017, doi:
10.14569/IJACSA.2017.080424.
[9] M. K. Singh and S. Chetia, “Detection and classification of plant leaf diseases in image processing using MATLAB,” International
Journal of Life Sciences Research, vol. 5, no. 4, pp. 120–124, 2017.
[10] Y. G. Naresh and H. S. Nagendraswamy, “Classification of medicinal plants: An approach using modified LBP with symbolic
representation,” Neurocomputing, vol. 173, pp. 1789–1797, Jan. 2016, doi: 10.1016/j.neucom.2015.08.090.
[11] S. Foix, G. Alenyà, and C. Torras, “Task-driven active sensing framework applied to leaf probing,” Computers and Electronics in
Agriculture, vol. 147, pp. 166–175, Apr. 2018, doi: 10.1016/j.compag.2018.01.020.
[12] K. Padmavathi and K. Thangadurai, “Implementation of RGB and grayscale images in plant leaves disease detection – comparative
study,” Indian Journal of Science and Technology, vol. 9, no. 6, Feb. 2016, doi: 10.17485/ijst/2016/v9i6/77739.
[13] M. Ivasic-Kos, I. Ipsic, and S. Ribaric, “A knowledge-based multi-layered image annotation system,” Expert Systems with
Applications, vol. 42, no. 24, pp. 9539–9553, Dec. 2015, doi: 10.1016/j.eswa.2015.07.068.
[14] V. Pooja, R. Das, and V. Kanchana, “Identification of plant leaf diseases using image processing techniques,” in 2017 IEEE
Technological Innovations in ICT for Agriculture and Rural Development (TIAR), Apr. 2017, pp. 130–133. doi:
10.1109/TIAR.2017.8273700.
[15] M. R. Devi and S. Maheswari, “Classification of paddy seeds certification in variety of seeds by digital image processing,”
International Journal of Information & Computation Technology, vol. 5, no. 1, pp. 63–69, 2015.
[16] S. D. Khirade and A. B. Patil, “Plant disease detection using image processing,” in 2015 International Conference on Computing
Communication Control and Automation, Feb. 2015, pp. 768–771. doi: 10.1109/ICCUBEA.2015.153.

 ISSN: 2088-8708
3038
[17] S. Chaudhary, U. Kumar, and A. Pandey, “A review: crop plant disease detection using image processing,” International Journal
of Innovative Technology and Exploring Engineering (IJITEE), vol. 8, no. 7, pp. 472–477, 2019.
[18] T. D. Dahigaonkar and R. T. Kalyane, “Identification of ayurvedic medicinal plants by image processing of leaf samples,”
International Research Journal of Engineering and Technology (IRJET), vol. 5, no. 5, pp. 351–355, 2018.
[19] S. Zhang, H. Wang, W. Huang, and Z. You, “Plant diseased leaf segmentation and recognition by fusion of superpixel, K-means
and PHOG,” Optik, vol. 157, pp. 866–872, Mar. 2018, doi: 10.1016/j.ijleo.2017.11.190.
[20] G. Saradhambal, R. Dhivya, S. Latha, and R. Rajesh, “Plant disease detection and its solution using image classification,”
International Journal of Pure and Applied Mathematics, vol. 119, no. 14, pp. 879–883, 2018.
[21] R. G. de Luna, E. P. Dadios, and A. A. Bandala, “Automated image capturing system for deep learning-based tomato plant leaf
disease detection and recognition,” in TENCON 2018 - 2018 IEEE Region 10 Conference, Oct. 2018, pp. 1414–1419. doi:
10.1109/TENCON.2018.8650088.
[22] H. Durmus, E. O. Gunes, and M. Kirci, “Disease detection on the leaves of the tomato plants by using deep learning,” in 2017 6th
International Conference on Agro-Geoinformatics, Aug. 2017, pp. 1–5. doi: 10.1109/Agro-Geoinformatics.2017.8047016.
[23] P. K. Sethy, N. K. Barpanda, A. K. Rath, and S. K. Behera, “Deep feature based rice leaf disease identification using support vector
machine,” Computers and Electronics in Agriculture, vol. 175, Aug. 2020, doi: 10.1016/j.compag.2020.105527.
[24] S. Wang, Y. Li, J. Yuan, L. Song, X. Liu, and X. Liu, “Recognition of cotton growth period for precise spraying based on
convolution neural network,” Information Processing in Agriculture, vol. 8, no. 2, pp. 219–231, Jun. 2021, doi:
10.1016/j.inpa.2020.05.001.
[25] K. R. Mamatha, S. Singh, and S. A. Hariprasad, “Detection and analysis of plant leaf diseases using convolutional neural network,”
Journal of Computational and Theoretical Nanoscience, vol. 17, no. 9, pp. 3899–3903, Jul. 2020, doi: 10.1166/jctn.2020.8983.
[26] A. Fuentes, S. Yoon, S. Kim, and D. Park, “A robust deep-learning-based detector for real-time tomato plant diseases and pests
recognition,” Sensors, vol. 17, no. 9, Sep. 2017, doi: 10.3390/s17092022.
[27] E. C. Too, L. Yujian, S. Njuki, and L. Yingchun, “A comparative study of fine-tuning deep learning models for plant disease
identification,” Computers and Electronics in Agriculture, vol. 161, pp. 272–279, Jun. 2019, doi: 10.1016/j.compag.2018.03.032.
[28] J. Ma, K. Du, F. Zheng, L. Zhang, Z. Gong, and Z. Sun, “A recognition method for cucumber diseases using leaf symptom images
based on deep convolutional neural network,” Computers and Electronics in Agriculture, vol. 154, pp. 18–24, Nov. 2018, doi:
10.1016/j.compag.2018.08.048.
[29] S. H. Lee, Y. L. Chang, C. S. Chan, and P. Remagnino, “Plant identification system based on a convolutional neural network for
the LifeClef 2016 plant classification task,” in CLEF (Working Notes), 2016, pp. 1–9.
[30] G. G. and A. P. J., “Identification of plant leaf diseases using a nine-layer deep convolutional neural network,” Computers &
Electrical Engineering, vol. 76, pp. 323–338, Jun. 2019, doi: 10.1016/j.compeleceng.2019.04.011.
[31] K. Li, J. Lin, J. Liu, and Y. Zhao, “Using deep learning for image-based different degrees of ginkgo leaf disease classification,”
Information, vol. 11, no. 2, Feb. 2020, doi: 10.3390/info11020095.
[32] S. Mohana Saranya, R. R. Rajalaxmi, R. Prabavathi, T. Suganya, S. Mohanapriya, and T. Tamilselvi, “Deep learning techniques in
tomato plant – A review,” Journal of Physics: Conference Series, vol. 1767, no. 1, Feb. 2021, doi: 10.1088/1742-
6596/1767/1/012010.
[33] Y. M. Oo and N. C. Htun, “Plant leaf disease detection and classification using image processing,” International Journal of Research
and Engineering, vol. 5, no. 9, pp. 516–523, Nov. 2018, doi: 10.21276/ijre.2018.5.9.4.
[34] Z. Iqbal, M. A. Khan, M. Sharif, J. H. Shah, M. H. ur Rehman, and K. Javed, “An automated detection and classification of citrus
plant diseases using image processing techniques: A review,” Computers and Electronics in Agriculture, vol. 153, pp. 12–32, Oct.
2018, doi: 10.1016/j.compag.2018.07.032.
[35] L. C. Ngugi, M. Abelwahab, and M. Abo-Zahhad, “Recent advances in image processing techniques for automated leaf pest and
disease recognition – A review,” Information Processing in Agriculture, vol. 8, no. 1, pp. 27–51, Mar. 2021, doi:
10.1016/j.inpa.2020.04.004.
[36] H. Sabrol and S. Kumar, “Recent studies of image and soft computing techniques for plant disease recognition and classification,”
International Journal of Computer Applications, vol. 126, no. 1, pp. 44–55, Sep. 2015, doi: 10.5120/ijca2015905982.
[37] A. S. Tripathy and D. K. Sharma, “Image processing techniques aiding smart agriculture,” in Modern Techniques for Agricultural
Disease Management and Crop Yield Prediction, 2020, pp. 23–48. doi: 10.4018/978-1-5225-9632-5.ch002.
[38] Q. Wang, J. Chu, L. Xu, and Q. Chen, “A new blind image quality framework based on natural color statistic,” Neurocomputing,
vol. 173, pp. 1798–1810, Jan. 2016, doi: 10.1016/j.neucom.2015.09.057.
[39] Z. Wang, H. Li, Y. Zhu, and T. Xu, “Review of plant identification based on image processing,” Archives of Computational Methods
in Engineering, vol. 24, no. 3, pp. 637–654, Jul. 2017, doi: 10.1007/s11831-016-9181-4.
[40] V. K. Vishnoi, K. Kumar, and B. Kumar, “Plant disease detection using computational intelligence and image processing,” Journal
of Plant Diseases and Protection, vol. 128, no. 1, pp. 19–53, Feb. 2021, doi: 10.1007/s41348-020-00368-0.
[41] S. Kumar and R. Kaur, “Plant disease detection using image processing- A review,” International Journal of Computer Applications,
vol. 124, no. 16, pp. 6–9, Aug. 2015, doi: 10.5120/ijca2015905789.
[42] S. Zhang, X. Wu, Z. You, and L. Zhang, “Leaf image based cucumber disease recognition using sparse representation
classification,” Computers and Electronics in Agriculture, vol. 134, pp. 135–141, Mar. 2017, doi: 10.1016/j.compag.2017.01.014.
[43] G. Kaur, S. Kaur, and A. Kaur, “Plant disease detection: a review of current trends,” International Journal of Engineering &
Technology, vol. 7, no. 3, pp. 875–881, 2018, doi: 10.14419/ijet.v7i3.34.19580.
[44] U. Khan and A. Oberoi, “Plant disease detection techniques: A review,” International Journal of Computer Science and Mobile
Computing, vol. 8, no. 4, pp. 59–68, 2019, doi: 10.13140/RG.2.2.10724.30081.
[45] G. Dhingra, V. Kumar, and H. D. Joshi, “Study of digital image processing techniques for leaf disease detection and classification,”
Multimedia Tools and Applications, vol. 77, no. 15, pp. 19951–20000, Aug. 2018, doi: 10.1007/s11042-017-5445-8.
[46] G. Sun, X. Jia, and T. Geng, “Plant diseases recognition based on image processing technology,” Journal of Electrical and Computer
Engineering, vol. 2018, pp. 1–7, 2018, doi: 10.1155/2018/6070129.
[47] K. V. Kumar and T. Jayasankar, “An identification of crop disease using image segmentation,” International Journal of
Pharmaceutical Sciences and Research, vol. 10, no. 3, pp. 1054–1064, 2018, doi: 10.13040/IJPSR.0975-8232.10(3).1054-64.
[48] E. Hamuda, M. Glavin, and E. Jones, “A survey of image processing techniques for plant extraction and segmentation in the field,”
Computers and Electronics in Agriculture, vol. 125, pp. 184–199, Jul. 2016, doi: 10.1016/j.compag.2016.04.024.
[49] P. Jiang, Y. Chen, B. Liu, D. He, and C. Liang, “Real-time detection of apple leaf diseases using deep learning approach based on
improved convolutional neural networks,” IEEE Access, vol. 7, pp. 59069–59080, 2019, doi: 10.1109/ACCESS.2019.2914929.
[50] K. Golhani, S. K. Balasundram, G. Vadamalai, and B. Pradhan, “A review of neural networks in plant disease detection using
hyperspectral data,” Information Processing in Agriculture, vol. 5, no. 3, pp. 354–371, Sep. 2018, doi: 10.1016/j.inpa.2018.05.002.

3039
[51] S. Kiani, S. M. van Ruth, S. Minaei, and M. Ghasemi-Varnamkhasti, “Hyperspectral imaging, a non-destructive technique in
medicinal and aromatic plant products industry: Current status and potential future applications,” Computers and Electronics in
Agriculture, vol. 152, pp. 9–18, Sep. 2018, doi: 10.1016/j.compag.2018.06.025.
[52] M. Francis and C. Deisy, “Disease detection and classification in agricultural Plants Using convolutional neural networks — a
Visual understanding,” in 2019 6th International Conference on Signal Processing and Integrated Networks (SPIN), Mar. 2019,
pp. 1063–1068. doi: 10.1109/SPIN.2019.8711701.
[53] M. H. Saleem, J. Potgieter, K. M. Arif, “Plant disease detection and classification by deep learning,” Plants, vol. 8, no. 11, Oct.
2019, doi: 10.3390/plants8110468.
[54] X. E. Pantazi, D. Moshou, T. Alexandridis, R. L. Whetton, and A. M. Mouazen, “Wheat yield prediction using machine learning
and advanced sensing techniques,” Computers and Electronics in Agriculture, vol. 121, pp. 57–65, Feb. 2016, doi:
10.1016/j.compag.2015.11.018.
[55] J. V. Vardhan, K. Kaur, and U. Kumar, “Plant recognition using hog and artificial neural network,” International Journal on Recent
and Innovation Trends in Computing and Communication, vol. 5, no. 5, pp. 746–750, 2017, doi: 10.17762/ijritcc.v5i5.600.
[56] J. Lu, J. Hu, G. Zhao, F. Mei, and C. Zhang, “An in-field automatic wheat disease diagnosis system,” Computers and Electronics
in Agriculture, vol. 142, pp. 369–379, Nov. 2017, doi: 10.1016/j.compag.2017.09.012.
[57] S. Yun, W. Xianfeng, Z. Shanwen, and Z. Chuanlei, “PNN based crop disease recognition with leaf image features and
meteorological data,” International Journal of Agricultural and Biological Engineering, vol. 8, no. 4, pp. 60–68, 2015, doi:
10.3965/j.ijabe.20150804.1719.
[58] T. Wiesner-Hanks et al., “Image set for deep learning: field images of maize annotated with disease symptoms,” BMC Research
Notes, vol. 11, no. 1, Dec. 2018, doi: 10.1186/s13104-018-3548-6.
[59] R. V. Meeradevi, M. R. Mundada, S. P. Sawkar, R. S. Bellad, and P. S. Keerthi, “Design and development of efficient techniques
for leaf disease detection using deep convolutional neural networks,” in 2020 IEEE International Conference on Distributed
Computing, VLSI, Electrical Circuits and Robotics (DISCOVER), Oct. 2020, pp. 153–158. doi:
10.1109/DISCOVER50404.2020.9278067.
[60] M. Nagaraju and P. Chawla, “Systematic review of deep learning techniques in plant disease detection,” International Journal of
System Assurance Engineering and Management, vol. 11, no. 3, pp. 547–560, Jun. 2020, doi: 10.1007/s13198-020-00972-1.
[61] S. Nigam and R. Jain, “Plant disease identification using deep learning: A review,” Indian Journal of Agricultural Sciences,
vol. 90, no. 2, pp. 249–257, 2020.
[62] M. Dyrmann, H. Karstoft, and H. S. Midtiby, “Plant species classification using deep convolutional neural network,” Biosystems
Engineering, vol. 151, pp. 72–80, Nov. 2016, doi: 10.1016/j.biosystemseng.2016.08.024.
[63] F. J. Knoll, V. Czymmek, S. Poczihoski, T. Holtorf, and S. Hussmann, “Improving efficiency of organic farming by using a deep
learning classification approach,” Computers and Electronics in Agriculture, vol. 153, pp. 347–356, Oct. 2018, doi:
10.1016/j.compag.2018.08.032.
[64] N. Petrellis, “Mobile application for plant disease classification based on symptom signatures,” in Proceedings of the 21st Pan-
Hellenic Conference on Informatics, Sep. 2017, pp. 1–6. doi: 10.1145/3139367.3139368.
[65] Y. Toda and F. Okura, “How convolutional neural networks diagnose plant disease,” Plant Phenomics, vol. 2019, pp. 1–14, Mar.
2019, doi: 10.34133/2019/9237136.
[66] J. G. A. Barbedo, “Factors influencing the use of deep learning for plant disease recognition,” Biosystems Engineering, vol. 172,
pp. 84–91, Aug. 2018, doi: 10.1016/j.biosystemseng.2018.05.013.
[67] V. Singh, Varsha, and A. K. Misra, “Detection of unhealthy region of plant leaves using image processing and genetic algorithm,”
in 2015 International Conference on Advances in Computer Engineering and Applications, Mar. 2015, pp. 1028–1032. doi:
10.1109/ICACEA.2015.7164858.
[68] A. Kamilaris and F. X. Prenafeta-Boldú, “Deep learning in agriculture: A survey,” Computers and Electronics in Agriculture, vol.
147, pp. 70–90, Apr. 2018, doi: 10.1016/j.compag.2018.02.016.
[69] S. K. Pilli, B. Nallathambi, S. J. George, and V. Diwanji, “eAGROBOT-A robot for early crop disease detection using image
processing,” in 2015 2nd International Conference on Electronics and Communication Systems (ICECS), Feb. 2015,
pp. 1684–1689. doi: 10.1109/ECS.2015.7124873.
[70] F. Martinelli et al., “Advanced methods of plant disease detection. A review,” Agronomy for Sustainable Development, vol. 35,
no. 1, pp. 1–25, Jan. 2015, doi: 10.1007/s13593-014-0246-1.
[71] P. Xu, G. Wu, Y. Guo, X. Chen, H. Yang, and R. Zhang, “Automatic wheat leaf rust detection and grading diagnosis via embedded
image processing system,” Procedia Computer Science, vol. 107, pp. 836–841, 2017, doi: 10.1016/j.procs.2017.03.177.
[72] Q. Liang, S. Xiang, Y. Hu, G. Coppola, D. Zhang, and W. Sun, “PD2SE-Net: Computer-assisted plant disease diagnosis and severity
estimation network,” Computers and Electronics in Agriculture, vol. 157, pp. 518–529, Feb. 2019, doi:
10.1016/j.compag.2019.01.034.
[73] Y. Guo et al., “Plant disease identification based on deep learning algorithm in smart farming,” Discrete Dynamics in Nature and
Society, vol. 2020, pp. 1–11, Aug. 2020, doi: 10.1155/2020/2479172.
BIOGRAPHIES OF AUTHORS
Anita S. Kini is currently an assistant professor with the Department of Computer
Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher
Education, Manipal, India. She is also pursuing her Ph.D. at Manipal Academy of Higher
Education, Manipal. Her research interests include computer vision-based applications for
agriculture, machine learning, and artificial intelligence. She can be contacted at
Anita.kini@manipal.edu.

 ISSN: 2088-8708
3040
Prema K. V. Reddy is working as a professor at Department of Computer Science
and Engineering at Manipal Institute of Technology, Manipal Academy of Higher Education
(MAHE-recognized as “Institute of Eminence” by Union HRD Ministry of India), Manipal.
She has completed her Ph.D. at Mysore University, Mysore, Karnataka, and her M.Tech. from
University Visvesvaraya College of Engineering, Bangalore. She is having more than 26 years
of teaching and 16 years of research experience. She has published various research papers in
national and international journals and conferences. Her areas of interest are cryptography and
network security, ad hoc networks, soft computing, and body area network. She can be
contacted at prema.kv@manipal.edu.
Smitha N. Pai is currently a professor in the department of Information and
Communication Technology, MIT, Manipal. She worked in various industries for nearly 5
years. Her master’s project was conducted in Hochschule Bremen, Germany. She has been a
resource person for various talks at many of the nearby colleges. Her area of expertise is in the
area of wireless sensor networks. Currently, she has students carrying out projects under her in
the area of IoT, and machine learning. She has published papers in journals and presented
papers at various conferences. She is also a member of various professional committees. She
can be contacted at Smitha.pai@manipal.edu.

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